User performance analysis and correction for S/W

Abstract

Methods and systems for optimizing user interaction with a software application. One system includes an electronic processor configured to receive a collection of interaction data including a plurality of interaction sequences and data associated with each of the plurality of interaction sequences, determine a performance metric for each of the plurality of interaction sequences, and train, with the collection of interaction data and the performance metric determined for each of the plurality of interaction sequences, an artificial intelligence (“AI”) model using supervised learning. The electronic processor is also configured to receive a current interaction sequence of a user for the software application and generate, via the AI model as applied to the interaction pattern of the user, a recommendation, for display within a user interface, for a modified user interaction pattern of the user for the software application.

What is claimed is: 
     
       1. A system for optimizing user interaction with a software application, the system comprising:
 an electronic processor configured to: 
 receive a collection of interaction data including a plurality of interaction sequences and data associated with each of the plurality of interaction sequences, wherein each of the interaction sequences relates to user interactions with a computer user interface when performing a task for a software function; 
 determine, in a training operation of an artificial intelligence (AI) model, a user skill level performance metric representing the user's level of expertise for each of the plurality of interaction sequences according to an identified ground truth, wherein the performance metric is a value assigned to each of the plurality of interaction sequences representing a skill level of users performing each of the plurality of interaction sequences; 
 train, with the collection of interaction data and the skill level performance metric determined for each of the plurality of interaction sequences as training data for performing supervised learning, the AI model to output a performance rating for a received interaction sequence, wherein the AI model is a time-series deep leaning model selected from the group consisting of a recurrent neural network model, a long short-term memory model, and a time-based convolution network model, and the training is provided to the time-series deep learning model, and wherein AI model is trained to learn an optimal sequence of interactions performed by an expert user by mapping each interaction sequence in the collection of interaction data to time and accuracy for each interaction sequence; 
 receive a current interaction sequence of a user for the software application, apply the trained AI model to the current interaction pattern of the user to assign a performance rating to the current interaction sequence of the user based on comparing the current interaction sequences of the user to the optimal sequence of interactions learnt by the AI model during training; 
 generate, via the AI model as applied to the current interaction pattern of the user, a recommendation for display within a user interface, for a modified user interaction pattern of the user for the software application based on the assigned performance rating; 
 output feedback along with the recommendation to the use upon analyzing the current interaction sequence of the user; 
 and retrain the AI model using the feedback. 
 
     
     
       2. The system of  claim 1 , wherein the performance rating includes at least one selected from a group consisting of a speed metric, an efficiency metric, and an accuracy metric. 
     
     
       3. The system of  claim 1 , wherein the electronic processor is configured to determine the performance metric by, for each interaction sequence included in the plurality of interaction sequences: predicting a user role for a user associated with the interaction sequence; predicting an intended task associated with the interaction sequence; and determining a time associated with performing the intended task. 
     
     
       4. The system of  claim 3 , wherein the electronic processor is configured to rank, for each user role and each intended task, each interaction sequence included in the plurality interaction sequences based on the performance metric. 
     
     
       5. The system of  claim 4 , wherein the electronic processor is configured to identify interaction sequences associated with an expert skill level based on the ranking of each interaction sequence. 
     
     
       6. The system of  claim 5 , wherein: the electronic processor is configured to generate the recommendation based on a comparison of the current interaction sequence to an interaction sequence included in the collection of interaction data; and the interaction sequence is associated with the same task and user role associated with the current interaction sequence. 
     
     
       7. The system of  claim 1 , wherein the electronic processor is configured to: predict a user role for the user associated with the current interaction sequence; predict an intended task associated with the current interaction sequence; and determine a performance metric associated with the current interaction sequence. 
     
     
       8. A method for optimizing user interaction with a software application, the method comprising:
 receiving, with an electronic processor, a collection of interaction data including a plurality of interaction sequences and data associated with each of the plurality of interaction sequences, wherein each of the interaction sequences relates to user interactions with a computer user interface when performing a task for a software function; 
 determining, in a training operation of an artificial intelligence (AI) model with the electronic processor, a user skill level performance metric representing the user's level of expertise for each of the plurality of interaction sequences according to an identified ground truth, wherein the performance metric is a value assigned to each of the plurality of interaction sequences representing a skill level of users performing each of the plurality of interaction sequences; 
 training, with the electronic processor, with the collection of interaction data and the skill level performance metric determined for each of the plurality of interaction sequences, as training data for performing supervised learning, the AI model to output a performance rating for a received interaction sequence, wherein the AI model is a time-series deep leaning model selected from the group consisting of a recurrent neural network model, a long short-term memory model, and a time-based convolution network model, and the training is provided to the time-series deep learning model, and wherein AI model is trained to learn an optimal sequence of interactions performed by an expert user by mapping each interaction sequence in the collection of interaction data to time and accuracy for each interaction sequence; 
 receiving, with the electronic processor, a current interaction sequence of a user for the software application; 
 applying the trained AI model with the electronic processor, to the current interaction pattern of the user to assign a performance rating to the current interaction sequence of the user based on comparing the current interaction sequences of the user to the optimal sequence of interactions learnt by the AI model during training; 
 generating, with the electronic processor, via the AI model as applied to the current interaction pattern of the user, a recommendation for display within a user interface, for a modified user interaction pattern of the user for the software application based on the assigned performance rating; 
 outputting, with the electronic processor, feedback along with the recommendation to the use upon analyzing the current interaction sequence of the user; 
 and retraining, with the electronic processor, the AI model using the feedback. 
 
     
     
       9. The system of  claim 8 , wherein the determining of the performance metric comprises: for each interaction sequence included in the plurality of interaction sequences: predicting a user role for a user associated with the interaction sequence; predicting an intended task associated with the interaction sequence; and determining a time associated with performing the intended task. 
     
     
       10. The system of  claim 9 , further comprising: ranking, for each user role and each intended task, each interaction sequence included in the plurality interaction sequences based on the performance metric. 
     
     
       11. The system of  claim 8 , further comprising: predicting a user role for the user associated with the current interaction sequence; predicting an intended task associated with the current interaction sequence; and determining a performance metric associated with the current interaction sequence; wherein the recommendation is based on the performance metric associated with the current interaction sequence. 
     
     
       12. A non-transitory, computer-readable medium storing instructions that, when executed by an electronic processor, perform a set of functions, the set of functions comprising:
 receiving a collection of interaction data including a plurality of interaction sequences and data associated with each of the plurality of interaction sequences, wherein each of the interaction sequences relates to user interactions with a computer user interface when performing a task for a software function; 
 determining, in a training operation of an artificial intelligence (AI) model, a user skill level performance metric representing the user's level of expertise for each of the plurality of interaction sequences according to an identified ground truth, wherein the performance metric is a value assigned to each of the plurality of interaction sequences representing a skill level of users performing each of the plurality of interaction sequences; 
 training with the collection of interaction data and the skill level performance metric determined for each of the plurality of interaction sequences as training data for performing supervised learning, the AI model to output a performance rating for a received interaction sequence, wherein the AI model is a time-series deep leaning model selected from the group consisting of a recurrent neural network model, a long short-term memory model, and a time-based convolution network model, and the training is provided to the time-series deep learning model, and wherein AI model is trained to learn an optimal sequence of interactions performed by an expert user by mapping each interaction sequence in the collection of interaction data to time and accuracy for each interaction sequence; 
 receiving a current interaction sequence of a user for the software application; 
 applying the trained AI model to the current interaction pattern of the user to assign a performance rating to the current interaction sequence of the user based on comparing the current interaction sequences of the user to the optimal sequence of interactions learnt by the AI model during training; 
 generating via the AI model as applied to the current interaction pattern of the user, a recommendation, for display within a user interface, for a modified user interaction pattern of the user for the software application based on the assigned performance rating; 
 outputting feedback along with the recommendation to the use upon analyzing the current interaction sequence of the user; 
 and retraining the AI model using the feedback. 
 
     
     
       13. The computer-readable medium of  claim 12 , wherein the determining of the performance metric comprises: for each interaction sequence included in the plurality of interaction sequences: predicting a user role for a user associated with the interaction sequence; predicting an intended task associated with the interaction sequence; and determining a time associated with performing the intended task. 
     
     
       14. The computer-readable medium of  claim 13 , the set of functions further comprising: ranking, for each user role and each intended task, each interaction sequence included in the plurality interaction sequences based on the performance metric. 
     
     
       15. The computer-readable medium of  claim 14 , the set of functions further comprising: identifying interaction sequences associated with an expert skill level based on the ranking of each interaction sequence. 
     
     
       16. The computer-readable medium of  claim 15 , wherein: the generating of the recommendation includes generating the recommendation based on a comparison of the current interaction sequence to an interaction sequence included in the collection of interaction data; the interaction sequence is associated with the same task and user role associated with the current interaction sequence; and the AI model is a time-series deep learning model. 
     
     
       17. The system of  claim 1 , wherein the time-series deep learning model is the recurrent neural network model, and the training is provided to the recurrent neural network model. 
     
     
       18. The system of  claim 1 , wherein the time-series deep learning model is the long short-term memory model, and the training is provided to the long short-term memory model. 
     
     
       19. The system of  claim 1 , wherein the time-series deep learning model is the time-based convolution network model, and the training is provided to the time-based convolution network model.